P-value functions are modern statistical tools that unify effect estimation and hypothesis testing and can provide alternative point and interval estimates compared to standard meta-analysis methods, using any of the many p-value combination procedures available (Xie et al., 2011, JASA). We provide a systematic comparison of different combination procedures, both from a theoretical perspective and through simulation. We show that many prominent p-value combination methods (e.g. Fisher's method) are not invariant to the orientation of the underlying one-sided p-values. Only Edgington's method, a lesser-known combination method based on the sum of p-values, is orientation-invariant and provides confidence intervals not restricted to be symmetric around the point estimate. Adjustments for heterogeneity can also be made and results from a simulation study indicate that the approach can compete with more standard meta-analytic methods.

翻译：p值函数是现代统计学工具，它将效应估计与假设检验统一起来，并能提供与标准元分析方法不同的点估计和区间估计，可利用多种现有的p值组合程序实现（Xie等人，2011，JASA）。我们从理论角度和模拟实验出发，系统比较了不同的组合程序。研究表明，许多著名的p值组合方法（如Fisher方法）对基础单侧p值的方向不具有不变性。只有Edgington方法——一种基于p值求和的较少被了解的组合方法——具有方向不变性，并能提供不局限于点估计对称的置信区间。该方法还可针对异质性进行调整，模拟研究结果表明该方法的性能可与更标准的元分析方法相媲美。